Robust sound event detection in bioacoustic sensor networks

By Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, Juan Pablo Bello. 2019

104 citations recorded by the source corpus.

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 ms) and long-term (30 min) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer, i.e. an affine layer whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input. We show that PCEN reduces temporal overfitting across dawn vs. dusk audio clips whereas context adaptation on PCEN-based summary statistics reduces spatial overfitting across sensor locations. Moreover, combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.

Subfields: Developmental Biology, Signal Processing

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Citations in the corpus

Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers (2020, methodology): “BirdVoxClassify is a dependency of BirdVoxDetect as of v0.”
Computational bioacoustics with deep learning: a review and roadmap (2021, methodology): “Lostanlen et al. (2019b) add a “context-adaptive neural network” layer to their CNN, whose weights are dynamically adapted at prediction time by an auxiliary network taking long-term summary statistics of spectrotemporal features as input.”
Active Learning for Sound Event Detection (2020, background): “SED has been applied in various applications, including noise monitoring [1], healthcare monitoring [2], wildlife monitoring [3], urban analysis [4], and multimedia indexing and retrieval [5].”